1. Field of the Invention
The present invention relates generally to property valuation and, more particularly, to automated property valuation. The present invention further relates to systems and methods for removing systematic bias from property valuation models.
2. Description of Related Art
Property valuation involves estimating the current fair market dollar value of a property. Changes in market conditions affect the value of properties. As a result, a property's value must be updated on a regular basis to reflect changes in market conditions, such as recent real estate transactions, trends toward larger houses, changes in interest rates, and so on.
The availability of accurate and up-to-date fair market property values is essential to banks, appraisers, investors in the secondary mortgage market, and other financial entities that deal with properties. For example, predictions of property values may be used to judge the values of residential properties that lenders submit to underwriters, to issue warnings on excessive valuations, and to allow abbreviated or no appraisal recommendations in other cases, often for a fee. For loan acquisitions, property values may be used to make guarantee fee adjustments. For post-purchase reviews, property values may be used to find collateral-related errors. Property value predictions may also be used for loss mitigation. Furthermore, predictions of property values are needed to assess the mark-to-market loan-to-value in a book of business.
Current appraisal processes for valuing large numbers of properties typically involve some type of model-based approach. For example, known model-based approaches in the mortgage include: repeated transactions (sales) index (RTI) models, tax assessment models, hedonic price models based on property characteristics, and neural network models based on property characteristics. These and other known automated property valuation models (APVMs) may be used individually to predict the values of properties. Alternatively, if more than one model prediction is available, it is also known to obtain a weighted average of those predictions. In either case, the data that support such valuation models may come from a variety of sources. Typical sources include loan acquisitions and public records data, which may be purchased from well-known data vendors. These data typically come in monthly cycles. As a result, property value databases are also typically updated every month.
Due to the important uses that are often made of property value predictions from APVMs, it is desirable that the predictions be as accurate as possible. Unfortunately, APVM predictions of mark-to-market values of residential properties are often subject to errors. For example, both idiosyncratic errors and systematic bias can affect the fair market value predictions of APVMs.
Idiosyncratic errors appear at the property level and typically result from inaccurate data. These errors tend to cancel out at a reasonable aggregate or cohort level. The main causes of idiosyncratic errors tend to be data quality and accuracy. There are at least three data related issues that may cause idiosyncratic errors. First, “true” property values are often measured with noise. Although a purchase transaction between parties of arm's length typically provides the best scenario for the true market value of the underlying property, other things can enter into a price negotiated in a sales contract. For example, the relative negotiating strength of the buyer and seller may be unequal. Moreover, the sale may reflect terms such as certain large value items that will be conveyed (e.g., furnishings), whether the seller will provide any help at the closing, rent-back arrangements, and the like.
A second data related issue that can lead to idiosyncratic errors is that each property has a unique quality or appeal. These unique property-specific characteristics may also change over time due to, for example, home improvements, neglect and abuse. To value a property, it is necessary to rely on other transacted properties in the corresponding geographical unit. The larger the unit, the more data are available for a stable index to be estimated. However, the larger the unit, the less representative the index is for the property level values.
A third type of data integrity issue that can lead to idiosyncratic errors involves property fraud, address mismatching, and other outliers.
Apart from the data integrity issues, which are property specific, systematic bias can also decrease the accuracy of model-based predictions of fair market value predictions. Systematic bias refers to either under-prediction or over-prediction that is sustained at the aggregate or cohort level. There are at least three possible causes of systematic bias in model-based value prediction: transformation bias, price tier effect, and time lag.
Transformation bias arises due to a mismatch between APVMs—which are typically specified in terms of logarithm of property value—and proportional prediction error (PPE). PPE is the common criterion used to evaluate APVMs. The impact of the transformation bias is model specific, but in general tends to be relatively small compared with the other two aforementioned sources of systematic bias.
Price tier effect refers to an empirical anomaly in which some APVMs tend to under-predict low-priced properties and over-predict high-priced properties (or vice versa), where the price tier is defined by the predicted property values.
Time lag refers to the inherent time delay in any APVM framework. This includes both processing cycle and intrinsic data delay. To illustrate these two sources, consider a typical prior art second quarter database update cycle depicted by a time line diagram 30 in FIG. 1 (PRIOR ART). The first source of time lag in this example is the value database processing cycle 32 from the data closing date (June 30) to the date that the value database is first used in business applications (August 23) at 34. In other words, about eight weeks are needed in this example just to push the model predictions through the processing cycle. The second source of time lag in this example is the data delay. Due to slow data arrivals, when the second quarter transaction data are initially uploaded in early July, only a portion of all the second quarter transactions is available. Moreover, the lack of data is proportionally greater for June data relative to April data. This non-equal representation is indicated by the triangular shape of the data gathering process 36.
Although home prices on average tend to rise over the long term, they do on occasion turn down in limited geographic areas. During periods of large up-swings in house prices, models with time lags will tend to under-predict house prices. By contrast, during periods of down-swing such models will tend to over-predict house prices. This can result in increased credit risk for lenders and cause other problems for businesses that rely on the accuracy of property value predictions.
In view of the foregoing, it would be desirable to provide an improved valuation adjustment scheme for use with APVMs that reduces bias. Such a scheme would facilitate more accurate predictions of current values of residential properties. Improved predictions of property values would, in turn, cause lenders to take value warnings more seriously, increase revenues from granting more appraisal waivers, reduce fraud in deals, raise repurchase and make-whole revenues, improve public reporting, and so on.